{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "import tensorflow as tf "
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "a = tf.random.normal((2,2,5))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "f = tf.keras.layers.Flatten()(a)\r\n",
    "f"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 10), dtype=float32, numpy=\n",
       "array([[-0.21072446,  0.45557994, -0.02337526, -2.325398  , -1.2609154 ,\n",
       "        -0.74487776, -0.60207206, -1.1650603 ,  1.1235406 , -0.42424965],\n",
       "       [-1.7584032 , -0.25413346,  0.6128092 , -0.10013708, -0.1972421 ,\n",
       "        -1.1226214 , -1.0332394 , -0.47482964, -0.6257364 , -0.8034088 ]],\n",
       "      dtype=float32)>"
      ]
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
 ],
 "metadata": {
  "orig_nbformat": 4,
  "language_info": {
   "name": "python",
   "version": "3.7.1",
   "mimetype": "text/x-python",
   "codemirror_mode": {
    "name": "ipython",
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   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
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  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.7.1 64-bit ('KerasEnv': virtualenv)"
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  "interpreter": {
   "hash": "49709926ca079d7fc3208f0b0481edbe733516c473689c5f1439ce0f2246de88"
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